Gravity Balancing Reliability and Sensitivity Analysis of Robotic Manipulators With Uncertainties

2021 ◽  
Author(s):  
Vu Linh Nguyen ◽  
Chin-Hsing Kuo ◽  
Po Ting Lin
Keyword(s):  
Sensitivity Analysis ◽  
Industrial Robot ◽  
Robotic Manipulators ◽  
Spring Stiffness ◽  
Stiffness Coefficients ◽  
Simulation Based ◽  
Dimensional Parameters ◽  
Reliability Performance ◽  
Mcs Method

Abstract This paper presents the gravity balancing reliability and sensitivity analysis of robotic manipulators with uncertainties. The gravity balancing reliability of the robot is defined as the probability that the reduction torque ratio of the robot reduces below a specified threshold. This index is of great importance for assessing and guaranteeing the balancing performance of the robot in the presence of uncertainties in input parameters. In this work, the balancing design for an industrial robot using the gear-spring modules (GSMs) is proposed with the adoption of a simulation-based analysis of the gravity effect of the robot. The Monte Carlo Simulation (MCS) method with normally distributed variables (i.e., link dimensions, masses, and spring stiffness coefficients) is employed to analyze and simulate the reliability. A case study with an industrial robot is then given to illustrate the reliability performance and the sensibility of the uncertain parameters. It is found that the gravity balancing behavior is achieved even when the uncertainties are applied. The uncertainties could deteriorate the balancing performance when increasing the standard deviations by more than seven percent of their means. The dimensional parameters enjoy the most critical influence on the balancing performance.

scholarly journals Simulation-based multi-criteria decision making: an interactive method with a case study on infectious disease epidemics

2021 ◽  
Author(s):  
Fabian Dunke ◽  
Stefan Nickel
Keyword(s):  
Infectious Disease ◽  
Decision Making ◽  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Multiple Objectives ◽  
Decision Making Process ◽  
Multi Criteria Decision Making ◽  
Analysis Question ◽  
Simulation Based

AbstractWhenever a system needs to be operated by a central decision making authority in the presence of two or more conflicting goals, methods from multi-criteria decision making can help to resolve the trade-offs between these goals. In this work, we devise an interactive simulation-based methodology for planning and deciding in complex dynamic systems subject to multiple objectives and parameter uncertainty. The outline intermittently employs simulation models and global sensitivity analysis methods in order to facilitate the acquisition of system-related knowledge throughout the iterations. Moreover, the decision maker participates in the decision making process by interactively adjusting control variables and system parameters according to a guiding analysis question posed for each iteration. As a result, the overall decision making process is backed up by sensitivity analysis results providing increased confidence in terms of reliability of considered decision alternatives. Using the efficiency concept of Pareto optimality and the sensitivity analysis method of Sobol’ sensitivity indices, the methodology is then instantiated in a case study on planning and deciding in an infectious disease epidemic situation similar to the 2020 coronavirus pandemic. Results show that the presented simulation-based methodology is capable of successfully addressing issues such as system dynamics, parameter uncertainty, and multi-criteria decision making. Hence, it represents a viable tool for supporting decision makers in situations characterized by time dynamics, uncertainty, and multiple objectives.

scholarly journals A Planning Tool for Reliability Assessment of Overhead Distribution Lines in Hybrid AC/DC Grids

2021 ◽  
Vol 13 (11) ◽  
pp. 6099
Author(s):  
Giovanna Adinolfi ◽  
Roberto Ciavarella ◽  
Giorgio Graditi ◽  
Antonio Ricca ◽  
Maria Valenti
Keyword(s):  
Reliability Assessment ◽  
Reliability Function ◽  
Climatic Conditions ◽  
Planning Tool ◽  
Grid Resource ◽  
Distribution Lines ◽  
Ac Networks ◽  
Reliability Performance ◽  
Power Dispatching

Integration of DC grids into AC networks will realize hybrid AC/DC grids, a new energetic paradigm which will become widespread in the future due to the increasing availability of DC-based generators, loads and storage systems. Furthermore, the huge connection of intermittent renewable sources to distribution grids could cause security and congestion issues affecting line behaviour and reliability performance. This paper aims to propose a planning tool for congestion forecasting and reliability assessment of overhead distribution lines. The tool inputs consist of a single line diagram of a real or synthetic grid and a set of 24-h forecasting time series concerning climatic conditions and grid resource operative profiles. The developed approach aims to avoid congestions criticalities, taking advantage of optimal active power dispatching among “congestion-nearby resources”. A case study is analysed to validate the implemented control strategy considering a modified IEEE 14-Bus System with introduction of renewables. The tool also implements reliability prediction formulas to calculate an overhead line reliability function in congested and congestions-avoided conditions. A quantitative evaluation underlines the reliability performance achievable after the congestion strategy action.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Sensitivity Analysis of Fuzzy Activity-Based Life Cycle Costing

2018 ◽  
Vol 225 ◽  
pp. 05002
Author(s):  
Freselam Mulubrhan ◽  
Ainul Akmar Mokhtar ◽  
Masdi Muhammad
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle ◽  
Interest Rate ◽  
Life Cycle Costing ◽  
High Impact ◽  
Final Output ◽  
Left And Right ◽  
The Interest Rate ◽  
Operation Cost

A sensitivity analysis is typically conducted to identify how sensitive the output is to changes in the input. In this paper, the use of sensitivity analysis in the fuzzy activity based life cycle costing (LCC) is shown. LCC is the most frequently used economic model for decision making that considers all costs in the life of a system or equipment. The sensitivity analysis is done by varying the interest rate and time 15% and 45%, respectively, to the left and right, and varying 25% of the maintenance and operation cost. It is found that the operation cost and the interest rate give a high impact on the final output of the LCC. A case study of pumps is used in this study.

Improving Casthouse Throughput Using Discrete-Event Modelling

2011 ◽  
Vol 693 ◽  
pp. 3-9 ◽  
Author(s):  
Bruce Gunn ◽  
Yakov Frayman
Keyword(s):  
Sensitivity Analysis ◽  
Discrete Event ◽  
Casting Machine ◽  
Production Data ◽  
Model Output ◽  
Arrival Times ◽  
Event Simulation ◽  
Modelling System ◽  
Key Parameter

The scheduling of metal to different casters in a casthouse is a complicated problem, attempting to find the balance between pot-line, crucible carrier, furnace and casting machine capacity. In this paper, a description will be given of a casthouse modelling system designed to test different scenarios for casthouse design and operation. Using discrete-event simulation, the casthouse model incorporates variable arrival times of metal carriers, crucible movements, caster operation and furnace conditions. Each part of the system is individually modelled and synchronised using a series of signals or semaphores. In addition, an easy to operate user interface allows for the modification of key parameters, and analysis of model output. Results from the model will be presented for a case study, which highlights the effect different parameters have on overall casthouse performance. The case study uses past production data from a casthouse to validate the model outputs, with the aim to perform a sensitivity analysis on the overall system. Along with metal preparation times and caster strip-down/setup, the temperature evolution within the furnaces is one key parameter in determining casthouse performance.

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